Epistasis pp 35-45 | Cite as

Biological Knowledge-Driven Analysis of Epistasis in Human GWAS with Application to Lipid Traits

  • Li MaEmail author
  • Alon Keinan
  • Andrew G. Clark
Part of the Methods in Molecular Biology book series (MIMB, volume 1253)


While the importance of epistasis is well established, specific gene–gene interactions have rarely been identified in human genome-wide association studies (GWAS), mainly due to low power associated with such interaction tests. In this chapter, we integrate biological knowledge and human GWAS data to reveal epistatic interactions underlying quantitative lipid traits, which are major risk factors for coronary artery disease. To increase power to detect interactions, we only tested pairs of SNPs filtered by prior biological knowledge, including GWAS results, protein–protein interactions (PPIs), and pathway information. Using published GWAS and 9,713 European Americans (EA) from the Atherosclerosis Risk in Communities (ARIC) study, we identified an interaction between HMGCR and LIPC affecting high-density lipoprotein cholesterol (HDL-C) levels. We then validated this interaction in additional multiethnic cohorts from ARIC, the Framingham Heart Study, and the Multi-Ethnic Study of Atherosclerosis. Both HMGCR and LIPC are involved in the metabolism of lipids and lipoproteins, and LIPC itself has been marginally associated with HDL-C. Furthermore, no significant interaction was detected using PPI and pathway information, mainly due to the stringent significance level required after correcting for the large number of tests conducted. These results suggest the potential of biological knowledge-driven approaches to detect epistatic interactions in human GWAS, which may hold the key to exploring the role gene–gene interactions play in connecting genotypes and complex phenotypes in future GWAS.

Key words

Epistasis Gene–Gene Interaction Biological Knowledge GWAS Lipid 


  1. 1.
    Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP et al (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106:9362–9367PubMedCentralPubMedCrossRefGoogle Scholar
  2. 2.
    Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA et al (2009) Finding the missing heritability of complex diseases. Nature 461:747–753PubMedCentralPubMedCrossRefGoogle Scholar
  3. 3.
    Frazer KA, Murray SS, Schork NJ, Topol EJ (2009) Human genetic variation and its contribution to complex traits. Nat Rev Genet 10:241–251PubMedCrossRefGoogle Scholar
  4. 4.
    Maher B (2008) Personal genomes: the case of the missing heritability. Nature 456:18–21PubMedCrossRefGoogle Scholar
  5. 5.
    Eichler EE, Flint J, Gibson G, Kong A, Leal SM et al (2010) Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet 11:446–450PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    Ma L, Ballantyne CM, Belmont JW, Keinan A, Brautbar A (2012) Interaction between SNPs in the RXRA and near ANGPTL3 gene region inhibit apolipoprotein B reduction following statin-fenofibric acid therapy in individuals with mixed dyslipidemia. J Lipid Res 53(11):2425–2428PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM et al (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466:707–713PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Asselbergs FW, Guo YR, van Iperen EPA, Sivapalaratnam S, Tragante V et al (2012) Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci. Am J Hum Genet 91:823–838PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Cheverud JM, Routman EJ (1995) Epistasis and its contribution to genetic variance components. Genetics 139:1455–1461PubMedCentralPubMedGoogle Scholar
  10. 10.
    Cockerham CC (1954) An extension of the concept of partitioning hereditary variance for analysis of covariances among relatives when epistasis is present. Genetics 39:859–882PubMedCentralPubMedGoogle Scholar
  11. 11.
    Zuk O, Hechter E, Sunyaev SR, Lander ES (2012) The mystery of missing heritability: genetic interactions create phantom heritability. Proc Natl Acad Sci 109:1193–1198PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Bateson W, Saunders ER, Punnett RC, Hurst CC (eds) (1905) Reports to the Evolution Committee of the Royal Society, report II. Harrison and Sons, LondonGoogle Scholar
  13. 13.
    Carlborg O, Haley CS (2004) Epistasis: too often neglected in complex trait studies? Nat Rev Genet 5:618–625PubMedCrossRefGoogle Scholar
  14. 14.
    Cordell HJ (2009) Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet 10:392–404PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Moore JH, Williams SM (2009) Epistasis and its implications for personal genetics. Am J Hum Genet 85:309–320PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Gao H, Granka JM, Feldman MW (2010) On the classification of epistatic interactions. Genetics 184:827–837PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Shimomura K, Low-Zeddies SS, King DP, Steeves TDL, Whiteley A et al (2001) Genome-wide epistatic interaction analysis reveals complex genetic determinants of circadian behavior in mice. Genome Res 11:959–980PubMedCrossRefGoogle Scholar
  18. 18.
    Carlborg Ö, Kerje S, Schütz K, Jacobsson L, Jensen P et al (2003) A global search reveals epistatic interaction between QTL for early growth in the chicken. Genome Res 13:413–421PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    Caicedo AL, Stinchcombe JR, Olsen KM, Schmitt J, Purugganan MD (2004) Epistatic interaction between Arabidopsis FRI and FLC flowering time genes generates a latitudinal cline in a life history trait. Proc Natl Acad Sci U S A 101:15670PubMedCentralPubMedCrossRefGoogle Scholar
  20. 20.
    Clark AG, Doane WW (1984) Interactions between the amylase and adipose chromosomal regions of Drosophila melanogaster. Evolution 957–982Google Scholar
  21. 21.
    Ma L, Dvorkin D, Garbe J, Da Y (2007) Genome-wide analysis of single-locus and epistasis single-nucleotide polymorphism effects on anti-cyclic citrullinated peptide as a measure of rheumatoid arthritis. BMC Proc 1:S127PubMedCentralPubMedCrossRefGoogle Scholar
  22. 22.
    Ma L, Yang J, Runesha HB, Tanaka T, Ferrucci L et al (2010) Genome-wide association analysis of total cholesterol and high-density lipoprotein cholesterol levels using the Framingham Heart Study data. BMC Med Genet 11:55PubMedCentralPubMedCrossRefGoogle Scholar
  23. 23.
    Ma L, Runesha HB, Dvorkin D, Garbe JR, Da Y (2008) Parallel and serial computing tools for testing single-locus and epistatic SNP effects of quantitative traits in genome-wide association studies. BMC Bioinformatics 9:315PubMedCentralPubMedCrossRefGoogle Scholar
  24. 24.
    Marchini J, Donnelly P, Cardon LR (2005) Genome-wide strategies for detecting multiple loci that influence complex diseases. Locus 2:0.0Google Scholar
  25. 25.
    Jia P, Zheng S, Long J, Zheng W, Zhao Z (2011) DmGWAS: dense module searching for genome-wide association studies in protein–protein interaction networks. Bioinformatics 27:95PubMedCentralPubMedCrossRefGoogle Scholar
  26. 26.
    Sun YV, Kardia SLR (2010) Identification of epistatic effects using a protein–protein interaction database. Hum Mol Genet 19:4345PubMedCentralPubMedCrossRefGoogle Scholar
  27. 27.
    Wu X, Dong H, Luo L, Zhu Y, Peng G et al (2010) A novel statistic for genome-wide interaction analysis. PLoS Genet 6:e1001131PubMedCentralPubMedCrossRefGoogle Scholar
  28. 28.
    Williams OD (1989) The atherosclerosis risk in communities (ARIC) study – design and objectives. Am J Epidemiol 129:687–702Google Scholar
  29. 29.
    Dawber TR, Meadors GF, Moore FE (1951) Epidemiological approaches to heart disease: the Framingham study. Am J Public Health Nations Health 41:279–286PubMedCentralPubMedCrossRefGoogle Scholar
  30. 30.
    Bild DE, Bluemke DA, Burke GL, Detrano R, Roux AVD et al (2002) Multi-ethnic study of atherosclerosis: objectives and design. Am J Epidemiol 156:871–881PubMedCrossRefGoogle Scholar
  31. 31.
    Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K et al (2007) The NCBI dbGaP database of genotypes and phenotypes. Nat Genet 39:1181–1186PubMedCentralPubMedCrossRefGoogle Scholar
  32. 32.
    Howie BN, Donnelly P, Marchini J (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5:e1000529PubMedCentralPubMedCrossRefGoogle Scholar
  33. 33.
    Altshuler DM, Gibbs RA, Peltonen L, Dermitzakis E, Schaffner SF et al (2010) Integrating common and rare genetic variation in diverse human populations. Nature 467:52–58PubMedCrossRefGoogle Scholar
  34. 34.
    Altshuler DL, Durbin RM, Abecasis GR, Bentley DR, Chakravarti A et al (2010) A map of human genome variation from population-scale sequencing. Nature 467:1061–1073PubMedCrossRefGoogle Scholar
  35. 35.
    Cordell HJ (2002) Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. Hum Mol Genet 11:2463–2468PubMedCrossRefGoogle Scholar
  36. 36.
    Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575PubMedCentralPubMedCrossRefGoogle Scholar
  37. 37.
    Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA et al (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909PubMedCrossRefGoogle Scholar
  38. 38.
    Haas BE, Horvath S, Pietilainen KH, Cantor RM, Nikkola E et al (2012) Adipose co-expression networks across Finns and Mexicans identify novel triglyceride-associated genes. BMC Med Genomics 5:61. doi: 10.1186/1755-8794-1185-1161 PubMedCentralPubMedCrossRefGoogle Scholar
  39. 39.
    Aulchenko YS, Ripatti S, Lindqvist I, Boomsma D, Heid IM et al (2009) Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat Genet 41:47–55PubMedCentralPubMedCrossRefGoogle Scholar
  40. 40.
    Burkhardt R, Kenny EE, Lowe JK, Birkeland A, Josowitz R et al (2008) Common SNPs in HMGCR in Micronesians and Whites associated with LDL-cholesterol levels affect alternative splicing of exon13. Arterioscler Thromb Vasc Biol 28:U2078–U2332CrossRefGoogle Scholar
  41. 41.
    Burkhardt R, Kenny EE, Lowe JK, Birkeland A, Josowitz R et al (2008) Common SNPs in HMGCR in micronesians and whites associated with LDL-cholesterol levels affect alternative splicing of exon13. Arterioscler Thromb Vasc Biol 28:2078–2084PubMedCentralPubMedCrossRefGoogle Scholar
  42. 42.
    Das J, Yu H (2012) HINT: high-quality protein interactomes and their applications in understanding human disease. BMC Syst Biol 6:92PubMedCentralPubMedCrossRefGoogle Scholar
  43. 43.
    Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH et al (2002) The human genome browser at UCSC. Genome Res 12:996–1006PubMedCentralPubMedCrossRefGoogle Scholar
  44. 44.
    Matthews L, Gopinath G, Gillespie M, Caudy M, Croft D et al (2009) Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res 37:D619–D622PubMedCentralPubMedCrossRefGoogle Scholar
  45. 45.
    Lemaitre RN, Tanaka T, Tang WH, Manichaikul A, Foy M et al (2011) Genetic loci associated with plasma phospholipid n-3 fatty acids: a meta-analysis of genome-wide association studies from the CHARGE consortium. PLoS Genet 7Google Scholar
  46. 46.
    Lambert CG, Black LJ (2012) Learning from our GWAS mistakes: from experimental design to scientific method. Biostatistics 13:195–203PubMedCentralPubMedCrossRefGoogle Scholar
  47. 47.
    Burton PR, Clayton DG, Cardon LR, Craddock N, Deloukas P et al (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661–678CrossRefGoogle Scholar
  48. 48.
    He J, Wang K, Edmondson AC, Rader DJ, Li C et al (2011) Gene-based interaction analysis by incorporating external linkage disequilibrium information. Eur J Hum Genet 19:164–172PubMedCentralPubMedCrossRefGoogle Scholar
  49. 49.
    Oh S, Lee J, Kwon M-S, Weir B, Ha K et al (2012) A novel method to identify high order gene-gene interactions in genome-wide association studies: gene-based MDR. BMC Bioinformatics 13:S5PubMedCentralPubMedCrossRefGoogle Scholar
  50. 50.
    Ma L, Clark AG, Keinan A (2013) Gene-based testing of interactions in association studies of quantitative traits. PLoS Genet 9:e1003321PubMedCentralPubMedCrossRefGoogle Scholar
  51. 51.
    Li SY, Cui YH (2012) Gene-centric gene-gene interaction: a model-based Kernel machine method. Ann Appl Stat 6:1134–1161CrossRefGoogle Scholar
  52. 52.
    Rajapakse I, Perlman MD, Martin PJ, Hansen JA, Kooperberg C (2012) Multivariate detection of gene-gene interactions. Genet Epidemiol 36:622–630PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Animal and Avian SciencesUniversity of MarylandCollege ParkUSA
  2. 2.Department of Biological Statistics and Computational Biology, Cornell Center for Comparative and Population GenomicsCornell UniversityIthacaUSA
  3. 3.Department of Molecular Biology and GeneticsCornell UniversityIthacaUSA

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